neural network

Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.


There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. The present study reports application of a neural network classifier to the processing of previously collected data on very low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low BMD (DXA T score below - 1) measured within one year.


The data-set used in the paper titled "Short-Term Load Forecasting Using an LSTM Neural Network."


This dataset includes PV power production measured on the SolarTech Lab, Politecnico di Milano, Italy. Data are freely available for scientific research purpose and further data validation.

In particular, the dataset is composed of the following variables and specifics, with a time resolution of 1 minute:


The first bit of light is the gesture of being, on a massive screen of the black panorama. A small point of existence, a gesture of being. The universal appeal of gesture is far beyond the barriers of languages and planets. These are the microtransactions of symbols and patterns which have traces of the common ancestors of many civilizations.Gesture recognition is important to make communication between the computer system and humans, in the present era many studies are going on regarding the gesture recognition systems.


Our efforts are made on one-shot voice conversion where the target speaker is unseen in training dataset or both source and target speakers are unseen in the training dataset. In our work, StarGAN is employed to carry out voice conversation between speakers. An embedding vector is used to represent speaker ID. This work relies on two datasets in English and one dataset in Chinese, involving 38 speakers. A user study is conducted to validate our framework in terms of reconstruction quality and conversation quality.